PersistentAgent Class
- java.
lang. Object - com.
azure. ai. agents. persistent. models. PersistentAgent
- com.
Implements
public final class PersistentAgent
implements JsonSerializable<PersistentAgent>
Represents an agent that can call the model and use tools.
Method Summary
| Modifier and Type | Method and Description |
|---|---|
|
static
Persistent |
fromJson(JsonReader jsonReader)
Reads an instance of Persistent |
|
Offset |
getCreatedAt()
Get the created |
| String |
getDescription()
Get the description property: The description of the agent. |
| String |
getId()
Get the id property: The identifier, which can be referenced in API endpoints. |
| String |
getInstructions()
Get the instructions property: The system instructions for the agent to use. |
| Map<String,String> |
getMetadata()
Get the metadata property: A set of up to 16 key/value pairs that can be attached to an object, used for storing additional information about that object in a structured format. |
| String |
getModel()
Get the model property: The ID of the model to use. |
| String |
getName()
Get the name property: The name of the agent. |
| String |
getObject()
Get the object property: The object type, which is always assistant. |
|
Binary |
getResponseFormat()
Get the response |
| Double |
getTemperature()
Get the temperature property: What sampling temperature to use, between 0 and 2. |
|
Tool |
getToolResources()
Get the tool |
|
List<Tool |
getTools()
Get the tools property: The collection of tools enabled for the agent. |
| Double |
getTopP()
Get the topP property: An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. |
|
Json |
toJson(JsonWriter jsonWriter) |
Methods inherited from java.lang.Object
Method Details
fromJson
public static PersistentAgent fromJson(JsonReader jsonReader)
Reads an instance of PersistentAgent from the JsonReader.
Parameters:
Returns:
Throws:
getCreatedAt
public OffsetDateTime getCreatedAt()
Get the createdAt property: The Unix timestamp, in seconds, representing when this object was created.
Returns:
getDescription
public String getDescription()
Get the description property: The description of the agent.
Returns:
getId
public String getId()
Get the id property: The identifier, which can be referenced in API endpoints.
Returns:
getInstructions
public String getInstructions()
Get the instructions property: The system instructions for the agent to use.
Returns:
getMetadata
public Map<String,String> getMetadata()
Get the metadata property: A set of up to 16 key/value pairs that can be attached to an object, used for storing additional information about that object in a structured format. Keys may be up to 64 characters in length and values may be up to 512 characters in length.
Returns:
getModel
public String getModel()
Get the model property: The ID of the model to use.
Returns:
getName
public String getName()
Get the name property: The name of the agent.
Returns:
getObject
public String getObject()
Get the object property: The object type, which is always assistant.
Returns:
getResponseFormat
public BinaryData getResponseFormat()
Get the responseFormat property: The response format of the tool calls used by this agent.
Returns:
getTemperature
public Double getTemperature()
Get the temperature property: What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
Returns:
getToolResources
public ToolResources getToolResources()
Get the toolResources property: A set of resources that are used by the agent's tools. The resources are specific to the type of tool. For example, the `code_interpreter` tool requires a list of file IDs, while the `file_search` tool requires a list of vector store IDs.
Returns:
getTools
public List<ToolDefinition> getTools()
Get the tools property: The collection of tools enabled for the agent.
Returns:
getTopP
public Double getTopP()
Get the topP property: An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both.
Returns: